Unfalsified Adaptive Control (UAC) is a recently proposed robust adaptive Control strategy. In this paper, the UAC principles and algorithms are reviewed and Multi-Model UAC is followed as an intermediate between UAC and multiple model Control. Different approaches in UAC and MMUAC are studied. Also, Multi-Model Unfalsified Generalized Predictive Control (MMUGPC) is proposed, which is a new Control design strategy in the UAC framework. For an uncertain system, by utilizing several generalized Predictive Controllers and discrete switching between them with unfalsified Control, a new structure is proposed and appropriate equations are derived. Simulation results show the effectiveness of proposed Multi-Model Unfalsified Generalized Predictive Control.

Predictive Control is one of the effective methods to reduce the effect of time delay and data dropout in networked Control systems. In the proposed Predictive Control method, the Control data is predicted as long as it covers the time delay and data dropout in the network, and the estimation of state variables is performed using the Kalman filter. By sending a package containing the predicted Control data to the actuator and selecting the appropriate Control data, the desired Control performance of the system can be achieved. In this paper, the networked Control system is considered as a switching system with an arbitrary switching signal. A criterion for evaluating the stability of the closed-loop system with time delay and data dropout is presented based on the theory of switching systems. Time delay and data dropout are selected as switching parameters and a subset of switching dynamic systems is created in the discrete state space. By providing the Lyapunov function for all subsets and solving matrix inequalities, the stability of the system can be investigated. The simulation results show the effect of data dropout on the stability of the system by considering the time delay in the forward and feedback channels.

In this paper, two Predictive Control methods for dual output multilevel rectifier are compared. The investigated structure is a seven-level PUC rectifier, which was selected based on high reliability and low cost. Increasing the number of input voltage levels helps to reduce the amount of harmonics and consequently reduce the size of power filters. On the other hand, current conduction in this converter is performed continuously and the problems of current discontinuity such as complexity in analysis and the requirement to use large induction filters on the DC side are solved. First, the design is accomplished based on the FCS-MPC method and two different output voltages with ratios of 1 and 3 are obtained. Also, the Control of output DC voltages and unity input power factor is well provided. Then, to achieve better stability, the MPC method based on Lyapunov theory has been utilized. In this method, the target variables are defined in the Lyapunov function and the cost function is derived from the same Lyapunov function. The advantages of this approach compared to the conventional MPC method are no need for gain adjustment, easier implementation and fewer sensors (the load current is estimated using the PUC7 rectifier mathematical model). The simulation of both FCS-MPC and Predictive Control based on Lyapunov method is carried out using Matlab/Simulink and the results of both methods are presented and compared with each other. Finally, it can be seen that in the Lyapunov-based method, the tracking of the reference current is smoother and with less fluctuations, and the seven-level rectifier voltage also has a more regular and sinusoidal waveform.

Model Predictive Controllers rely on dynamic model of the process, most often linear models obtained by system identification. The advantage of MPC is the fact that it allows the current timeslot to be optimized by keeping future timeslots in account. In this paper, the transient performance of MPC method against DTC with non linear Hysteresis based Controllers is presented. Then a comparative performance in terms of torque and flux ripple are presented for PTC and DTC of three phase induction motor drive. In Predictive Control the Control objectives are defined as cost function, which is to be minimized to have a greater flexibility to include constraints which results in low computational complexity compared to DTC scheme. simulation results of PTC and DTC in MATLAB/ SIMULINK are shown and compared. This comparison between is carried out by validating the feasibility of PTC and highlighting some important implementation issues.

Drum boilers consume is one of the most important equipment of the thermal power plants in many countries and are widely used in industrial process applications. A boiler-turbine unit is a typical multivariable industrial Control system in power plant. The boiler is so manufactured that the drum level must be in the specified range. Output constraints are an important challenge in boiler drum systems to have safety and efficient operation. In this paper, the problem of state space model Predictive Control for a constrained nonlinear boiler drum systems is investigated. Predictive Control is a highly efficient and strong method since its Control performance has been reported to be best among other conventional techniques to Control the multivariable dynamical plants with various inputs and outputs constraints. Simulation results demonstrate the satisfactory performance and effectiveness of this method and compatibility with practical implementation in boiler.

Fast and accurate transient response is the main requirement in electric machine position Control. Conventional cascade Control structure has sluggish response due to the limitation of inner Control loop bandwidth. In this paper, in order to decrease the Permanent Magnet Synchronous Motor (PMSM) transient response time it can be used reference model using feed-forward signals. In this structure, feed-forward signals generated by simplified model of permanent magnet synchronous motor. In this paper, feed-forward signals generated are emplyed in model Predictive Control; which are combined with conventional cascade Control structure. Using this approach, a fast transient response and satisfactory tracking ability will be guaranteed. The proposed method is compared with the model reference method and conventional cascade structure. Simulation results showed a good performance of proposed method related to both methods. Verification of simulation results were carried out by experimental results.

Surge is one of the two destructive factors in compressors. surge is the stream instability phenomenon in compressor that imposes severe damages to the compressors. Nowadays, suppressing surge phenomenon is one of the most important issues in oil and gas industries, especially when flow reduction or gas reflux is considered. According to Moore-Greitzer compressor model, this paper designs an active Controller for surge Control in constant speed centrifugal compressors. As such, the applied operator considered for surge Control is Close Couple Valve (CCV) and it is designed to stabilize a centrifugal compressor system with disturbaces using nonlinear Predictive Controller. The proposed Controller, without any information about the amount of Throttle Valve variations, could Control the surge instability and reduce the distance between compressor operation point and the surge line. Finally, the compressor system with Controller is simulated and the obtained results will show the efficiency of the designed nonlinear Predictive Controller.

Nowadays Autonomous Underwater Vehicles (AUVs) are an unavoidable part of marine industries. One of the most important parts of any autonomous vehicle is the Control issue to achieve the desired performance. This paper is concerned with speed Control of an AUV model respecting the state and Control constraints. According to the Newton-Euler method, the 6 DOF kinematic and dynamic models of the AUV are established. A well-defined performance index and constrained finite horizon optimization program in the form of Model Predictive Control (MPC) strategy is proposed to regulate the horizontal speed of AUV to its desired value while the constraints on the states like depth and Control signals are considered in finite time horizon optimization program to be satisfied. The main problem for such a situation is the interaction between speed Control and depth deviation then quadratic programing technique managed responses to avoid state and Control signal constraints. Simulation results show a reliable performance of proposed MPC strategy to Control the horizontal speed of AUV while all the constraints on state, Control signal and also the variation of the Control signal are satisfied.

Model Predictive Controller is widely used in industrial plants. Uncertainty is one of the critical issues in real systems. In this paper, the direct adaptive Simplified Model Predictive Control (SMPC) is proposed for unknown or time varying plants with uncertainties. By estimating the plant step response in each sample, the Controller is designed and the Controller coefficients are directly calculated. The proposed method is validated via simulations for both slow and fast time varying systems. Simulation results indicate the Controller ability for tracking references in the presence of plant’s time varying parameters. In addition, an analytical tuning method for adjusting prediction horizon is proposed based on optimization of the objective function. It leads to a simple formula including the model parameters, and an indirect adaptive Controller can be designed based on the analytical formula. Simulation results indicate a better performance for the tuned Controller. Finally, experimental tests are performed to show the effectiveness of the proposed methodologies.

In this paper, a comparison is made between the Predictive Control method and a neuro-fuzzy Control algorithm, which has been proposed recently by the authors, for Controlling a benchmark three-story frame structure subjected to earthquakes. Through numerical simulations, it is demonstrated that the proposed neuro-fuzzy Control algorithm can provide comparable and, in some cases, superior results.